A survey of barriers to screening for oral cancer among rural Black Americans

A survey of barriers to screening for oral cancer among ruralBlack Americans

James A. Shepperd*, Jennifer L. Howell and Henrietta LoganDepartment of Psychology, University of Florida, Gainesville, FL, USA

*Correspondence to:Department of Psychology,University of Florida, POBox 112250, Gainesville, FL32611-2250. E-mail:[email protected]

Received: 22 March 2013Revised: 8 August 2013Accepted: 3 September 2013

AbstractObjective: Research documents a disparity between Black and White Americans in mortality for oralcancer that appears to result in part from behaviors such as lower oral cancer screening among BlackAmericans. We examined barriers to oral cancer screening among Black Americans.

Methods: We surveyed Black Americans (N = 366) living in rural Florida to identify barriers togetting screened for oral cancer.

Results: Low knowledge/social attention, lack of resources, and fear/defensive avoidance predictedscreening intentions, with lack of resources emerging as the largest barrier. Participants also reportedthat a recommendation from their provider was most likely to increase screening intentions, whereasencountering financial barriers was most likely to decrease screening intentions.

Conclusions: Low knowledge/social attention, lack of resources, and fear/defensive avoidanceemerged as independent barriers to oral cancer screening, with the latter two barriers accountingfor the most variance in intentions to get screened.Copyright © 2013 John Wiley & Sons, Ltd.

Introduction

Oral and pharyngeal cancer, or cancer of the mouth andthroat (e.g., throat, larynx, nose, sinuses, and mouth) [1,2]represents a national health problem. Because treatment oforal cancer requires delicate surgery to the face and can bequite disfiguring, it is perhaps the most costly cancer totreat [3]. Moreover, each year, 8000 people die from oralcancer, and many more suffer from the severe monetaryconsequences of the disease [4]. The death rate exceeds thatfor other cancers that receive far greater publicity, includingcervical cancer, Hodgkin’s lymphoma, testicular cancer,and thyroid cancer.Ironically, oral cancer is extremely treatable, if caught

early (i.e., in stage 1 or 2) [5]. Unfortunately, late stagediagnosis (i.e., stage 3 or 4) is all too common [6]. Althoughseveral factors are potentially associated with late-stagediagnoses of oral cancer, the most obvious is failure to obtainan oral cancer screening from a dentist or physician [7].Research suggests that people who are screened earlier aremore likely to detect oral cancer at an earlier stage [8],which is associated with reduced mortality [5]. Researchlinks lower stage diagnosis with earlier detection [8], andfavorable outcomes from oral cancer increase substantiallywith early detection [5].Mortality from oral cancer does not strike equally across

groups. Despite similar incidence rates among Black andWhite Americans, Black men die from oral cancer atalmost twice the rate of White men [5]. The discrepancyappears to arise from disparities in health behaviors suchas screening [9,10]. Black Americans are more likely than

White Americans to be diagnosed with later stages of oralcancer [6]. The implication is that delays in oral cancerscreening and detection contribute to later stage oral cancerdiagnosis [8] and thus more negative health outcomes.Disparities in health outcomes are particularly evident in ruralsettings, where longer travel times and lower accessibility tohealth care, among other things, can undermine getting a can-cer screening [11]. Detecting and treating oral cancer earlierin rural Black Americans could reduce the health disparitiesin oral cancer mortality. The present article explores barriersto oral cancer screening among rural Black Americans.

Barriers to screening for oral cancer

We know of two studies that have examined barriers toscreening for oral cancer among Black Americans. Thefirst investigated patients–providers communication andidentified poor communication and low knowledge aboutoral cancer as barriers to screening [12]. Importantly, thesefindings are only suggestive because the study examined alimited number of barriers among urban Black Americansand did so only indirectly.The second study involved qualitative analyses of focus

group data and yields a rich picture of the barriers faced byBlack Americans living in rural settings. The focus groupsreported 12 distinct obstacles to oral cancer screening, whichwere distilled to three broad categories of barriers [13]. Thefirst barrier was low knowledge/social attention. Manyparticipants reported that they had never heard of oralcancer, and those who had did not regard it to be importantbecause important others (physicians, community leaders,

Copyright © 2013 John Wiley & Sons, Ltd.

Psycho-OncologyPsycho-Oncology 23: 276–282 (2014)Published online 30 September 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/pon.3415

news sources) were not discussing or advocating screening.The second barrier was lack of resources. Many participantsreported that they lacked resources (money, insurance, time,or transportation) to get screened. The third barrier was fear/defensive avoidance. Participants reported that a desire toavoid unpleasant news or situations deterred them fromgetting screened.Although informative, the focus group findings are based

on a small sample and may be overly influenced by theresponses of a few, highly vocal participants. In addition,a concern with any qualitative study is that the researchermay inadvertently influence participant responses, leadingto biases in the data. We examined here whether the focusgroup findings would replicate in a larger survey of com-munity participants.

Overview

We surveyed Black Americans in three rural counties inFlorida to explore barriers to oral cancer screening. Onthe basis of the focus group study [13], we hypothesizedthat low knowledge/social attention, lack of resources,and fear/defensive avoidance would play a central role inparticipants’ explanations for not getting screened for oralcancer. Examining the barriers in a survey allowed us toexplore covariation among the barriers. For example, itis possible that participants are reporting a resource barrierto screening when the real reason for not getting screenedis that they fear the results.To aid possible future interventions, for a subset of the

barriers, we explored how much introducing or removingthe barrier would influence screening intentions. Forexample, for each resource barrier, we explored the extentto which providing the specific resource would increasescreening intentions among participants who reported thatthey faced the barrier. We also explored whether removingthe barrier would decrease screening intentions amongparticipants who reported not having the barrier. In doingso, we could examine which barriers, if removed, wouldbe most influential in increasing screening, and if added,would be most influential in deterring screening.

Method

Participants

We trained three interviewers to recruit, consent, and admin-ister the survey orally to residents from three counties innorth central Florida. One of the counties had one retireddentist, and one had five dentists. The third county had amid-size city with a university hospital and dental school.However, we recruited participants from the rural edges ofthe county, a minimum of a 20-min drive from the universitydental school. The interviewers consented 425 participantsfor the study. Because of procedural errors, however, wehad to discard data from 59 participants, almost all occurring

in the first 75 participants run, at which point, we mademodifications to the format of the survey to reduce inter-viewer errors and then retrained our interviewers. In theend, we had usable data from 366 Black American adults(216 women) ages 40–101 years (M= 52.1, SD= 10.6).The interviewers recruited participants by going door-to-door or in public places such as beauty salons andchurches. Participants received a $10 gift card to a localretail store for their participation.

Procedure

Pilot testing suggested that many members of our targetpopulation needed assistance in completing our survey(e.g., reading the questions, understanding a scaled responseformat). We thus trained community members to administerthe survey orally. In addition, pilot testing revealed that par-ticipants were more comfortable with the term mouth andthroat cancer than with oral and pharyngeal cancer. Thus,we used the term mouth and throat cancer in our survey.Focus groups conducted prior to the survey revealed

that many participants struggled with Likert-type responseformats. We thus presented our survey items in two steps.First, we asked participants if they agreed or disagreedwith a statement such as, I want to get a mouth and throatcancer exam in the next 12 months. Second, if participantsreported that they agreed, we asked if they slightly agreed,moderately agreed, or strongly agreed. Conversely, ifparticipants reported that they disagreed, we asked ifthey slightly disagreed, moderately disagreed, or stronglydisagreed. From these responses, we created a 6-pointscale that ranged from 1, Strongly Disagree to 6, StronglyAgree. Although time consuming, this approach eliminatedconfusion and allowed for greater variability in participantresponses. Unless otherwise specified, all items in thesurvey used this 6-point scale format.When administering the survey, the interviewer read

aloud a consent form that participants then signed. Theinterviewer then read instructions and read each itemaloud. Participants provided responses verbally, and theinterviewer recorded the responses on the survey instrument.Because of time constraints, we did not collect data onrisk behavior.

Measures

Demographic items

We recorded participants’ age, gender, education, andwhether they had health insurance and dental insurance(both coded so that no= 0, yes= 1).

Past oral cancer screening

We assessed whether participants ever had an oral cancerscreening with two items: (i) Has a medical provider evertold you that he or she examined you for mouth and throat

277Barriers to oral cancer screening

Copyright © 2013 John Wiley & Sons, Ltd. Psycho-Oncology 23: 276–282 (2014)DOI: 10.1002/pon

cancer?; and (ii) Has a doctor or dentist ever examinedyour mouth, by pulling on your tongue, with gauzewrapped around it, and feeling under your tongue andinside your cheeks? If participants responded yes to eitheritem, we coded their response as yes (no or don’t know=0,yes=1).

Intentions

We measured intentions using a two-item index (α=0.89):(i) I want to get an exam for mouth and throat cancer inthe next 12 months; and (ii) I am definitely going to get anexam for mouth and throat cancer in the next 12 months.

Knowledge/social attention

We used four items to assess knowledge/social attentionregarding oral cancer. The first two items asked partici-pants, (i) Have you heard of mouth and throat cancerbefore today?; and (ii) Do you know anyone with mouthand throat cancer? (no= 0, yes= 1). The next two itemsused the two-step, Likert scale format described earlier,and participants indicate whether (iii)My doctor or dentisthas recommended I get examined for mouth and throatcancer; and (iv) Important people in my life (like myfamily or my pastor) are telling me to get a mouth andthroat cancer exam. Because these four items used differentresponse formats, we treated them as separate predictors inour analyses.

Resources

Using the two-step Likert scale described earlier, we assessedthe extent to which participants had resources relevant togetting an oral cancer screening or treatment for oralcancer. For example, one item stated, I can afford treatmentfor mouth and throat cancer. These items asked participantswhether they could afford a screening for oral cancer, couldafford treatment for oral cancer (measured with two items),had transportation to a screening location, had time to getscreened, regarded getting screened as convenient, hadhealth insurance to pay for screening, and knew where togo to get screened. We combined these eight items tocreate a resource index (α= 0.79) and coded items suchthat higher values indicate greater resources.

Defensive avoidance

We assessed defensive avoidance using an eight-iteminformation avoidance scale. The scale is adaptable tovarious threats and shows strong psychometric character-istics including strong predictive validity and test–retestreliability. We tailored the scale to assess avoidance ofinformation about having oral cancer. An example itemwas I don’t want to know [if I have oral cancer]. Wecombined the items to form a single index of defensiveavoidance (α= 0.87) with higher values indicating greaterdefensive avoidance.

Consequence of changing barriers

For a subset of items (the two knowledge/social attentionitems asking about recommendations, 7 of the 8 resourceitems), we included probes to examine whether participantsbelieved that adding or removing the barriers wouldinfluence their screening intentions. For example, afterreporting the extent to which they agreed or disagreed withthe item, I can afford treatment for oral cancer, participantswho reported that they slightly, moderately, or stronglydisagreed next indicated if they would be More Likely (1),Equally Likely (0), or Less Likely (�1) to get screened ifthey could afford treatment. Conversely, participants whoreported that they slightly, moderately, or strongly agreednext indicated if they would be More Likely (1), EquallyLikely (0), or Less Likely (�1) to get screened if they couldnot afford treatment. We did not include these probes whenit seemed nonsensical to do so (e.g., the items askingparticipants whether they had heard of oral cancer or knewsomeone with oral cancer, and the items asking aboutdefensive avoidance).

Data analysis

We used correlation and multiple regressions to analyze pre-dictors of screening intentions for oral cancer. We used one-sample t-tests that compared themean for an itemwith zero toexamine whether introduction or removal of a barrier wouldinfluence screening intentions. We conducted all analysesusing IBM SPSS Statistics, 18.0 (IBM, Armonk, New York).

Results

Table 1 presents demographic information about ourparticipants. Most (75%) reported having a high schooleducation. Although a high school degree would presum-ably equate with adequate reading skills, we find that ruralresidents of north central Florida, even residents with highschool degrees, have low literacy skills. The majority

Table 1. Sample characteristics

Demographics Female (%) Male (%)

Gender 216 (59.0) 143 (39.0)Education

Less than ninth grade 30 (14.1) 24 (17.1)Some high school 20 (9.4) 14 (10.0)High school degree 110 (51.6) 68 (48.6)Some college 30 (14.1) 20 (14.3)College degree or more 23 (10.8) 14 (10.0)

Yes (%) No (%)Have health insurance 259 (70.8) 107 (29.2)Have dental insurance 189 (51.6) 175 (47.8)Heard of oral cancer prior to today 287 (78.4) 77 (21.0)Prior oral cancer screening 93 (25.4) 266 (72.7)

N= 366. Not all participants responded to all items, resulting in some missing data (e.g.,seven participants did not report gender).

278 J. A. Shepperd et al.


reported that they had some form of health insurance(70.8%), and about half (51.6%) reported that they haddental insurance.

Screening intentions

Table 2 presents the zero-order correlations between ourpredictor and outcome measures. Greater screening inten-tions correlated with participant reports that (i) theirprovider recommended screening; (ii) important peoplein their life recommended screening; (iii) they have moreresources; and (iv) they are lower in defensive avoidance.We next conducted hierarchical multiple regression to

predict screening intentions in which we entered ourpredictors into the model in blocks. In Block 1, we entereddemographic variables (i.e., age, gender, health insurancestatus, education level); in Block 2, we entered the itemstapping low knowledge/social attention; in Block 3, weentered our index of lack of resources; and in Block 4,we entered our index of defensive avoidance.As evident in Table 3, adding each of our blocks of

predictors significantly improved model fit, Fs> 2.30,p< 0.05, R2 s> 0.03. The final model included only threesignificant predictors. The largest barrier was lack of

resources (b=0.49); fewer resources corresponded withlower screening intentions. The second largest barrier wasdefensive avoidance (b=�0.24); the more participantswanted to avoid learning their screening results, the lowertheir screening intentions. The third barrier was whethera provider had recommended screening (b= 0.14); theabsence of a recommendation corresponded with lowerscreening intentions. Surprisingly, none of our other threeknowledge/social attention items—the largest barrierreported by participants in our earlier work—predictedscreening intentions.

Effectiveness of changing barriers

Table 4 reports the results from the item asking participantshow their screening intentions would change if each of theresource barriers was added or removed. The numbers onthe left side of the Table represent the responses of partic-ipants with the barrier and how they would respond if thebarrier were removed. The numbers on the right side ofthe Table represent the responses of participants withoutthe barrier and how they would respond if the barrier wereadded. For each barrier, participants who reported havingthe barrier indicated that removing it would increase their

Table 2. Zero-order correlations

Screeningintentions

Heard oforal cancer

Knowsomeone

Providers recommendscreening

Others recommendscreening

Lackresources

Screening intentions —

Heard of oral cancer 0.03 —

Know someone with oral cancer �0.02 0.26** —

Providers recommend screening 0.21** �0.09 �0.08 —

Others recommend screening 0.14** 0.02 �0.17** 0.39** —

Lack of resources 0.41** 0.28** 0.08 0.15** 0.17** —

Defensive avoidance �0.23** �0.11* �0.14** 0.06 �0.07 �0.17**

*p < 0.05.**p< 0.01.

Table 3. Screening intentions

Block Barrier b SE t ΔF ΔR2

1 Demographics 2.30* 0.03Age 0.004 0.01 0.65Gender 0.07 0.15 0.48Education 0.05 0.06 0.82Health insurance status �0.15 0.18 �0.87Prior screening 0.06 0.17 0.35

2 Low knowledge/social attention 5.29** 0.06Prior knowledge of oral cancer �0.22 0.19 1.18Know someone with oral cancer �0.11 0.16 0.69Providers recommend screening 0.14 0.05 3.04**Important others recommend screening �0.02 0.04 �0.48

3 Lack of resources 0.48 0.07 6.51** 42.29** 0.114 Defensive avoidance �0.24 0.07 3.50** 12.24** 0.03TOTAL F (11, 331)= 8.91, p< 0.001, R2=0.23

*p< 0.05.**p< 0.01.



likelihood of getting screened. Conversely, with twoexceptions, participants who reported not having the barrierindicated that having it would decrease their likelihood ofgetting screened. The two exceptions were the two knowl-edge/social attention items. Participants reported that theywould still get screened even if providers or important peoplein their lives were not recommending screening.Examining the effect sizes reveals that removing any

given barrier influenced intentions more than adding thatbarrier. Examining only the effects of removing barriers(the left column of numbers), participants reported that arecommendation from a provider would be most likelyto increase screening intentions. Likewise, examiningonly the effects of adding barriers, participants reportedthat losing financial resources would be most likely todecrease their screening intentions.1

Discussion

The findings from our survey of barriers to oral cancerscreening generally replicated the findings from the focusgroup study [13] with some interesting exceptions. Con-sistent with the focus group study, low knowledge/socialattention, lack of resources, and fear/defensive avoidanceemerged as independent barriers to getting screened. Incontrast to the focus group study, low knowledge/socialattention was not the dominant predictor of screeningintentions. Indeed, the only knowledge/social attentionvariable that uniquely predicted screening intentions waswhether a provider had recommended screening. Wesuspect that knowledge/social attention may have playeda less important role because most participants in oursurvey (78.4%) had heard of oral cancer prior to the study.Finally, lacking resources—particularly, lacking financialresources—emerged as the dominant barrier to screening.Lack of financial resources no doubt contributes to avariety of related problems including a lack of routinedental checkups where people are most likely to undergoan oral cancer examination.

It is noteworthy that the three barrier groups correlatedwith each other. However, the correlations were generallysmall. Moreover, our regression analyses revealed that thethree barriers independently predicted screening inten-tions. This finding is perhaps most important with regardto the third barrier—fear/defensive avoidance. We wereconcerned that many people would be unwilling to admitthat they are fearful and that they would prefer to remainignorant about an important health problem. Yet, wefound that participants were quite willing to acknowledgetheir defensiveness. Moreover, defensive avoidance pre-dicted screening intentions even though it entered last inthe regression model.

Consequences of the barriers

We could have accessed the importance of each of the bar-riers to screening in many ways. The most straightforwardapproach is merely to examine which barrier predicts themost variance in screening intentions. Our analysesrevealed that resource barriers were the strongest predictorof intentions, followed by fear/defensive avoidance andlow knowledge/social attention. A second, more novel,approach is to ask people the likelihood that they wouldget screened if a barrier was added or removed. Althoughthis approach was sensible for only a subset of barriers,the results yielded several important findings. First,removing barriers produces a bigger effect than addingbarriers. The implication is that people without a givenbarrier may be able to find ways to get screened were theyto encounter a barrier. Second, the analysis suggests thatadding financial barriers would produce the largestdecrease in cancer. Third, our findings suggest that aprovider recommendation to get screened would producethe largest increase in screening. Interestingly, participantswho reported receiving such recommendations said thatan absence of a provider recommendation would notinfluence their screening intentions. Perhaps participantswho report receiving provider recommendations in the

Table 4. Screening intentions after removing or adding resources barriers

Remove barrier Add barrier

Barrier M n d M n d

Low knowledge/social attentionOthers (do not) recommend screening 0.71 242 1.22* 0.08 114 0.10Providers (do not) recommend screening 0.85 299 1.93* �0.17 58 0.19

Resources(Do not) Know where to go 0.61 114 0.92* �0.42 249 0.53*(Do not) Have transportation 0.67 63 1.18* �0.42 294 0.50*(Do not) Have time 0.74 77 1.30* �0.27 284 0.32*(In)Convenient 0.74 101 1.28* �0.25 263 0.29*Can(not) afford treatment 0.75 158 1.32* �0.43 196 0.51*(Do not) Have money to pay 0.78 251 1.39* �0.49 110 0.64*(Do not) Have insurance 0.83 120 1.66* �0.49 241 0.66*

*p < 0.001.



past have problems that make them sensitive to the impor-tance or need for screening. Alternatively, other circum-stances, such as the availability of personal and financialresources, may make these participants less reliant onrecommendations from others.Yet, there remains another alternative explanation that

applies to all of the consequence probe items. The probeitems asked participants to engage in counterfactualthinking [14]; to imagine another reality, a reality wherethey had resources or lacked them, or where othersrecommended screening or did not. Although peopleengage in counterfactual thinking all the time (e.g.,imagining how the day would be different if they had leftfor work 10 min earlier), the alternative reality theyimagine may not accurately reflect what would actuallyoccur. People may fail to recognize what factors actuallyinfluence their behavior or might overestimate or underes-timate how influential those factors are [15]. For example,our participants reported that the absence of providerrecommendations would not influence their likelihood ofgetting screened. It is possible that these participantsunderestimate how crucially important a provider’srecommendation is in their decision making. Clearly, weneed research that tests the actual consequences ofchanging barriers.

Limitations

Our study sampled Black Americans living in rural northcentral Florida, and the findings may not generalize togroups living elsewhere. Indeed, a different set of barriersto screening for oral cancer may emerge for an affluentsample for which financial resources are less pertinent.In addition, we examined intentions rather than behavior.In our defense, this study was intended to identifyreported barriers with an eye toward developing interven-tions to increase screening behavior. Nevertheless, weacknowledge that intentions are only moderately linkedto behavior.

Conclusions

We began with the observation of race differences inmortality from oral cancer, a disparity that appears due inpart to delays and lower rates of oral cancer screeningamong Black Americans. Our goal was to identify barriersto screening among Black Americans in rural settings. Ourstudy is the second to show that low knowledge/socialattention, lack of resources, and fear/defensive avoidanceindependently influence screening intentions. Whereasthe prior study examined focus groups, we used surveymethodology and recruited a larger sample. In contrast tothe prior study, the present study suggests that lack ofresources is the primary barrier to screening followed byfear/defensive avoidance. Finally, participants report that arecommendation from their provider was most likely toincrease screening intentions, whereas encountering financialbarriers was most likely to decrease screening intentions.

Acknowledgements

This work was supported by the National Institute of Dental andCraniofacial Research at the National Institutes of Health, H LOGANPI 1U54DEO19261-01 and by a Graduate Research Fellowshipfrom the National Science Foundation awarded to Jennifer L.Howell, DGE-0802270.

All participants were treated according to American PsychologicalAssociation guidelines for the treatment of human participants.The University of Florida Institutional Review Board approvedthis research.

Conflict of interest

The authors have no conflict of interest.

Note

1. Analyzing the data after omitting participants whoreported having a prior oral cancer screening didnot change the results.

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A survey of barriers to screening for oral cancer among rural Black Americans

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